{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# eXtreme Gradient Boosting (Featureset 2)\n",
    "## Hyperparameter tuning is performed on reduced feature set 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 0.10924883,  1.83030605, -0.14807631, ...\n",
      "X_32_val                  -> array([[ 0.66944195,  0.46536115,  0.79919788, ...\n",
      "X_32test_new              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32train_new             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_32train_std             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[ -6.40490\n",
      "X_test_new                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_train                   -> array([[[ 0.00119031,  0.00873315,  0.00641749, ..\n",
      "X_train_new               -> array([[-0.74031227,  0.23616372, -0.18182195, ...\n",
      "X_train_std               -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([6, 6, 5, ..., 0, 4, 1])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 4, ..\n",
      "y_32_train                -> array([0, 3, 4, ..., 0, 3, 1])\n",
      "y_32_val                  -> array([2, 2, 4, ..., 0, 7, 3])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([6, 6, 5, ..\n",
      "y_train                   -> array([0, 3, 4, ..., 0, 3, 1])\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.datasets import dump_svmlight_file\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import collections\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 24) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 24) arrays for SNR values:\n",
      "[-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_32train_new.shape, \"and labels: \", y_32_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_32test_new), X_32test_new[18].shape, \"arrays for SNR values:\")\n",
    "print(sorted(X_32test_new.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set (80000, 24) and labels (80000,)\n"
     ]
    }
   ],
   "source": [
    "#Unfold all test data into one array\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_test1 = []\n",
    "y_test1 = []\n",
    "for snr in snrs:\n",
    "    X_test1.append(X_32test_new[snr])\n",
    "    y_test1.append(y_32_test[snr])\n",
    "\n",
    "X_test1 = np.vstack(X_test1)\n",
    "y_test1 = np.hstack(y_test1)\n",
    "X_test1, y_test1 = shuffle(X_test1, y_test1)\n",
    "print(\"Test set {} and labels {}\".format(X_test1.shape, y_test1.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune max_depth and min_child_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 49.34687537749608 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid1 = {'max_depth':list(range(3,10,2)), 'min_child_weight': list(range(1,6,2))}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search1 = GridSearchCV(estimator = XGBClassifier(max_depth=3, eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search1.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50572, std: 0.00285, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: 0.50545, std: 0.00273, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: 0.50605, std: 0.00170, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: 0.51582, std: 0.00154, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: 0.51576, std: 0.00094, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: 0.51560, std: 0.00149, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.51835, std: 0.00092, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: 0.51841, std: 0.00032, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: 0.51776, std: 0.00010, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.51839, std: 0.00054, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: 0.51899, std: 0.00102, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: 0.51985, std: 0.00013, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 9, 'min_child_weight': 5},\n",
       " 0.51985)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search1.grid_scores_, grid_search1.best_params_, grid_search1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down parameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 67.50378731489181 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid2 = {'max_depth':[8,9,10,11], 'min_child_weight': [4,5,6,7]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search2 = GridSearchCV(estimator = XGBClassifier(eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid2, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search2.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.51784, std: 0.00047, params: {'max_depth': 8, 'min_child_weight': 4},\n",
       "  mean: 0.51879, std: 0.00138, params: {'max_depth': 8, 'min_child_weight': 5},\n",
       "  mean: 0.51991, std: 0.00043, params: {'max_depth': 8, 'min_child_weight': 6},\n",
       "  mean: 0.51940, std: 0.00113, params: {'max_depth': 8, 'min_child_weight': 7},\n",
       "  mean: 0.51824, std: 0.00102, params: {'max_depth': 9, 'min_child_weight': 4},\n",
       "  mean: 0.51985, std: 0.00013, params: {'max_depth': 9, 'min_child_weight': 5},\n",
       "  mean: 0.51875, std: 0.00111, params: {'max_depth': 9, 'min_child_weight': 6},\n",
       "  mean: 0.52041, std: 0.00105, params: {'max_depth': 9, 'min_child_weight': 7},\n",
       "  mean: 0.51909, std: 0.00072, params: {'max_depth': 10, 'min_child_weight': 4},\n",
       "  mean: 0.51887, std: 0.00127, params: {'max_depth': 10, 'min_child_weight': 5},\n",
       "  mean: 0.52041, std: 0.00148, params: {'max_depth': 10, 'min_child_weight': 6},\n",
       "  mean: 0.51995, std: 0.00100, params: {'max_depth': 10, 'min_child_weight': 7},\n",
       "  mean: 0.51850, std: 0.00184, params: {'max_depth': 11, 'min_child_weight': 4},\n",
       "  mean: 0.51725, std: 0.00049, params: {'max_depth': 11, 'min_child_weight': 5},\n",
       "  mean: 0.52107, std: 0.00064, params: {'max_depth': 11, 'min_child_weight': 6},\n",
       "  mean: 0.52023, std: 0.00102, params: {'max_depth': 11, 'min_child_weight': 7}],\n",
       " {'max_depth': 11, 'min_child_weight': 6},\n",
       " 0.521075)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search2.grid_scores_, grid_search2.best_params_, grid_search2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 25.139945753415425 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid2_1 = {'max_depth':[11, 13, 15]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search2_1 = GridSearchCV(estimator = XGBClassifier(eta=0.3, objective='multi:softprob', min_child_weight=6,\n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid2_1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search2_1.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.52107, std: 0.00064, params: {'max_depth': 11},\n",
       "  mean: 0.51999, std: 0.00056, params: {'max_depth': 13},\n",
       "  mean: 0.51691, std: 0.00109, params: {'max_depth': 15}],\n",
       " {'max_depth': 11},\n",
       " 0.521075)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search2_1.grid_scores_, grid_search2_1.best_params_, grid_search2_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune gamma, while setting max_depth and min_child_weight to optimum values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 28.614404435952505 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid3 = {'gamma':[i/10.0 for i in range(0,5)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search3 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid3, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search3.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.52107, std: 0.00064, params: {'gamma': 0.0},\n",
       "  mean: 0.51948, std: 0.00052, params: {'gamma': 0.1},\n",
       "  mean: 0.51905, std: 0.00089, params: {'gamma': 0.2},\n",
       "  mean: 0.51992, std: 0.00132, params: {'gamma': 0.3},\n",
       "  mean: 0.51948, std: 0.00077, params: {'gamma': 0.4}],\n",
       " {'gamma': 0.0},\n",
       " 0.521075)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search3.grid_scores_, grid_search3.best_params_, grid_search3.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune subsample and colsample_bytree; set max_depth, min_child_weight and gamma to optimum values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 53.42190520763397 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid4 = {'subsample':[i/10.0 for i in range(6,10)], 'colsample_bytree':[i/10.0 for i in range(6,10)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search4 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid4, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search4.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.51818, std: 0.00260, params: {'colsample_bytree': 0.6, 'subsample': 0.6},\n",
       "  mean: 0.51944, std: 0.00130, params: {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
       "  mean: 0.51836, std: 0.00114, params: {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
       "  mean: 0.51894, std: 0.00150, params: {'colsample_bytree': 0.6, 'subsample': 0.9},\n",
       "  mean: 0.51829, std: 0.00172, params: {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
       "  mean: 0.51854, std: 0.00177, params: {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
       "  mean: 0.51919, std: 0.00271, params: {'colsample_bytree': 0.7, 'subsample': 0.8},\n",
       "  mean: 0.51876, std: 0.00066, params: {'colsample_bytree': 0.7, 'subsample': 0.9},\n",
       "  mean: 0.51871, std: 0.00169, params: {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
       "  mean: 0.51926, std: 0.00067, params: {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
       "  mean: 0.51995, std: 0.00016, params: {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  mean: 0.51910, std: 0.00034, params: {'colsample_bytree': 0.8, 'subsample': 0.9},\n",
       "  mean: 0.51818, std: 0.00093, params: {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
       "  mean: 0.51995, std: 0.00178, params: {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
       "  mean: 0.52024, std: 0.00262, params: {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       "  mean: 0.51919, std: 0.00156, params: {'colsample_bytree': 0.9, 'subsample': 0.9}],\n",
       " {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       " 0.5202374999999999)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search4.grid_scores_, grid_search4.best_params_, grid_search4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for subsample and colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 86.37014453808466 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid5 = {'subsample': [i/100.0 for i in range(75,100,5)],\n",
    "               'colsample_bytree':  [i/100.0 for i in range(75,100,5)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search5 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid5, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search5.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.51825, std: 0.00120, params: {'colsample_bytree': 0.75, 'subsample': 0.75},\n",
       "  mean: 0.51982, std: 0.00127, params: {'colsample_bytree': 0.75, 'subsample': 0.8},\n",
       "  mean: 0.51916, std: 0.00062, params: {'colsample_bytree': 0.75, 'subsample': 0.85},\n",
       "  mean: 0.51925, std: 0.00160, params: {'colsample_bytree': 0.75, 'subsample': 0.9},\n",
       "  mean: 0.51953, std: 0.00091, params: {'colsample_bytree': 0.75, 'subsample': 0.95},\n",
       "  mean: 0.51843, std: 0.00261, params: {'colsample_bytree': 0.8, 'subsample': 0.75},\n",
       "  mean: 0.51995, std: 0.00016, params: {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  mean: 0.51992, std: 0.00074, params: {'colsample_bytree': 0.8, 'subsample': 0.85},\n",
       "  mean: 0.51910, std: 0.00034, params: {'colsample_bytree': 0.8, 'subsample': 0.9},\n",
       "  mean: 0.51892, std: 0.00105, params: {'colsample_bytree': 0.8, 'subsample': 0.95},\n",
       "  mean: 0.52086, std: 0.00207, params: {'colsample_bytree': 0.85, 'subsample': 0.75},\n",
       "  mean: 0.51971, std: 0.00191, params: {'colsample_bytree': 0.85, 'subsample': 0.8},\n",
       "  mean: 0.51992, std: 0.00064, params: {'colsample_bytree': 0.85, 'subsample': 0.85},\n",
       "  mean: 0.52075, std: 0.00151, params: {'colsample_bytree': 0.85, 'subsample': 0.9},\n",
       "  mean: 0.51838, std: 0.00173, params: {'colsample_bytree': 0.85, 'subsample': 0.95},\n",
       "  mean: 0.51987, std: 0.00172, params: {'colsample_bytree': 0.9, 'subsample': 0.75},\n",
       "  mean: 0.52024, std: 0.00262, params: {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       "  mean: 0.52016, std: 0.00158, params: {'colsample_bytree': 0.9, 'subsample': 0.85},\n",
       "  mean: 0.51919, std: 0.00156, params: {'colsample_bytree': 0.9, 'subsample': 0.9},\n",
       "  mean: 0.51897, std: 0.00167, params: {'colsample_bytree': 0.9, 'subsample': 0.95},\n",
       "  mean: 0.52010, std: 0.00120, params: {'colsample_bytree': 0.95, 'subsample': 0.75},\n",
       "  mean: 0.52014, std: 0.00079, params: {'colsample_bytree': 0.95, 'subsample': 0.8},\n",
       "  mean: 0.51914, std: 0.00126, params: {'colsample_bytree': 0.95, 'subsample': 0.85},\n",
       "  mean: 0.51907, std: 0.00068, params: {'colsample_bytree': 0.95, 'subsample': 0.9},\n",
       "  mean: 0.51972, std: 0.00131, params: {'colsample_bytree': 0.95, 'subsample': 0.95}],\n",
       " {'colsample_bytree': 0.85, 'subsample': 0.75},\n",
       " 0.5208625)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search5.grid_scores_, grid_search5.best_params_, grid_search5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model to data and evaluate learning curves- inspect overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-merror:0.462388\tvalidation_0-mlogloss:1.92755\tvalidation_1-merror:0.502\tvalidation_1-mlogloss:1.93455\n",
      "[1]\tvalidation_0-merror:0.437212\tvalidation_0-mlogloss:1.81628\tvalidation_1-merror:0.49475\tvalidation_1-mlogloss:1.83036\n",
      "[2]\tvalidation_0-merror:0.42695\tvalidation_0-mlogloss:1.73125\tvalidation_1-merror:0.492038\tvalidation_1-mlogloss:1.75192\n",
      "[3]\tvalidation_0-merror:0.416537\tvalidation_0-mlogloss:1.65796\tvalidation_1-merror:0.49\tvalidation_1-mlogloss:1.68513\n",
      "[4]\tvalidation_0-merror:0.4087\tvalidation_0-mlogloss:1.59742\tvalidation_1-merror:0.488463\tvalidation_1-mlogloss:1.6311\n",
      "[5]\tvalidation_0-merror:0.401038\tvalidation_0-mlogloss:1.54234\tvalidation_1-merror:0.488512\tvalidation_1-mlogloss:1.58221\n",
      "[6]\tvalidation_0-merror:0.394675\tvalidation_0-mlogloss:1.49356\tvalidation_1-merror:0.487775\tvalidation_1-mlogloss:1.53902\n",
      "[7]\tvalidation_0-merror:0.390763\tvalidation_0-mlogloss:1.45166\tvalidation_1-merror:0.486688\tvalidation_1-mlogloss:1.5023\n",
      "[8]\tvalidation_0-merror:0.387087\tvalidation_0-mlogloss:1.41387\tvalidation_1-merror:0.485525\tvalidation_1-mlogloss:1.46948\n",
      "[9]\tvalidation_0-merror:0.383738\tvalidation_0-mlogloss:1.3803\tvalidation_1-merror:0.485537\tvalidation_1-mlogloss:1.44108\n",
      "[10]\tvalidation_0-merror:0.379137\tvalidation_0-mlogloss:1.34935\tvalidation_1-merror:0.485275\tvalidation_1-mlogloss:1.41492\n",
      "[11]\tvalidation_0-merror:0.376588\tvalidation_0-mlogloss:1.32144\tvalidation_1-merror:0.484787\tvalidation_1-mlogloss:1.39164\n",
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      "[47]\tvalidation_0-merror:0.281938\tvalidation_0-mlogloss:0.967956\tvalidation_1-merror:0.477412\tvalidation_1-mlogloss:1.16037\n",
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      "[61]\tvalidation_0-merror:0.243175\tvalidation_0-mlogloss:0.916962\tvalidation_1-merror:0.47645\tvalidation_1-mlogloss:1.1505\n",
      "[62]\tvalidation_0-merror:0.239912\tvalidation_0-mlogloss:0.913436\tvalidation_1-merror:0.476512\tvalidation_1-mlogloss:1.15009\n",
      "[63]\tvalidation_0-merror:0.236363\tvalidation_0-mlogloss:0.909385\tvalidation_1-merror:0.476575\tvalidation_1-mlogloss:1.14981\n",
      "[64]\tvalidation_0-merror:0.233663\tvalidation_0-mlogloss:0.905926\tvalidation_1-merror:0.476613\tvalidation_1-mlogloss:1.1496\n",
      "[65]\tvalidation_0-merror:0.230762\tvalidation_0-mlogloss:0.902476\tvalidation_1-merror:0.475863\tvalidation_1-mlogloss:1.14923\n",
      "[66]\tvalidation_0-merror:0.227375\tvalidation_0-mlogloss:0.898442\tvalidation_1-merror:0.476288\tvalidation_1-mlogloss:1.14903\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[67]\tvalidation_0-merror:0.225725\tvalidation_0-mlogloss:0.896033\tvalidation_1-merror:0.476537\tvalidation_1-mlogloss:1.14871\n",
      "[68]\tvalidation_0-merror:0.223275\tvalidation_0-mlogloss:0.893301\tvalidation_1-merror:0.476763\tvalidation_1-mlogloss:1.14855\n",
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      "[90]\tvalidation_0-merror:0.166725\tvalidation_0-mlogloss:0.823191\tvalidation_1-merror:0.477025\tvalidation_1-mlogloss:1.1467\n",
      "[91]\tvalidation_0-merror:0.164463\tvalidation_0-mlogloss:0.819841\tvalidation_1-merror:0.476662\tvalidation_1-mlogloss:1.14671\n",
      "[92]\tvalidation_0-merror:0.161313\tvalidation_0-mlogloss:0.81587\tvalidation_1-merror:0.4771\tvalidation_1-mlogloss:1.1468\n",
      "[93]\tvalidation_0-merror:0.158662\tvalidation_0-mlogloss:0.812619\tvalidation_1-merror:0.477175\tvalidation_1-mlogloss:1.14683\n",
      "[94]\tvalidation_0-merror:0.15575\tvalidation_0-mlogloss:0.809238\tvalidation_1-merror:0.476975\tvalidation_1-mlogloss:1.14692\n",
      "[95]\tvalidation_0-merror:0.153362\tvalidation_0-mlogloss:0.80601\tvalidation_1-merror:0.47755\tvalidation_1-mlogloss:1.14695\n",
      "[96]\tvalidation_0-merror:0.151087\tvalidation_0-mlogloss:0.803008\tvalidation_1-merror:0.477587\tvalidation_1-mlogloss:1.14702\n",
      "[97]\tvalidation_0-merror:0.148825\tvalidation_0-mlogloss:0.800075\tvalidation_1-merror:0.477463\tvalidation_1-mlogloss:1.14697\n",
      "[98]\tvalidation_0-merror:0.1461\tvalidation_0-mlogloss:0.796836\tvalidation_1-merror:0.477338\tvalidation_1-mlogloss:1.14692\n",
      "[99]\tvalidation_0-merror:0.1443\tvalidation_0-mlogloss:0.793688\tvalidation_1-merror:0.477663\tvalidation_1-mlogloss:1.14701\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, eta=0.3, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=11,\n",
       "       min_child_weight=6, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.75)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model with optimum values of hyperprameters \n",
    "model = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, subsample= 0.75, colsample_bytree= 0.85,\n",
    "                    eta=0.3, objective='multi:softprob', \n",
    "                    num_class=8, early_stopping_rounds=20)\n",
    "\n",
    "eval_set = [(X_32train_new, y_32_train), (X_test1, y_test1)]\n",
    "\n",
    "#We evaluate the model's performance on training and test sets by three evaluation metrics\n",
    "# https://github.com/dmlc/xgboost/blob/master/doc/parameter.md\n",
    "model.fit(X_32train_new, y_32_train, eval_set = eval_set, eval_metric=[\"merror\", \"mlogloss\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QEQsjL4AxlzqvkQk9e21jTJ8SsKQgIrOBS4EqVS08yvYvAT/EaaNoAL6hqssD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\nTqLYUdfE4tI9rCqvo7RmL6U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3274e6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JMI/7DxbuTH94n7uOXe4/pTpncEcKjyY3LXev+YQ5/+Abv3FKXVruHPzi050DfNO2TjIR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\nLFkYY4yp1f8DfWJLkg0LX9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c32d119e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results = model.evals_result()\n",
    "epochs = len(results['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Tune reg_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 26.53257782459259 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid6 = {'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search6 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0,\n",
    "                                                      subsample = 0.75, colsample_bytre6 = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid6, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search6.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.52055, std: 0.00231, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.51877, std: 0.00075, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.51880, std: 0.00193, params: {'reg_alpha': 0.1},\n",
       "  mean: 0.51902, std: 0.00176, params: {'reg_alpha': 1},\n",
       "  mean: 0.50413, std: 0.00110, params: {'reg_alpha': 100}],\n",
       " {'reg_alpha': 1e-05},\n",
       " 0.52055)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search6.grid_scores_, grid_search6.best_params_, grid_search6.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for reg_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 24.42363880077998 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid7 = {'reg_alpha':[1e-7, 1e-6, 1e-5, 1e-4]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search7 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0,\n",
    "                                                      subsample = 0.75, colsample_bytre6 = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid7, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search7.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.51972, std: 0.00117, params: {'reg_alpha': 1e-07},\n",
       "  mean: 0.51972, std: 0.00117, params: {'reg_alpha': 1e-06},\n",
       "  mean: 0.52055, std: 0.00231, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.51865, std: 0.00147, params: {'reg_alpha': 0.0001}],\n",
       " {'reg_alpha': 1e-05},\n",
       " 0.52055)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search7.grid_scores_, grid_search7.best_params_, grid_search7.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 30.158052655061088 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid8 = {'reg_lambda':[1e-5, 1e-2, 0.1, 1, 100]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search8 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, reg_alpha= 1e-05,\n",
    "                                                      subsample = 0.75, colsample_bytre6 = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid8, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search8.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.51899, std: 0.00127, params: {'reg_lambda': 1e-05},\n",
       "  mean: 0.51923, std: 0.00026, params: {'reg_lambda': 0.01},\n",
       "  mean: 0.51948, std: 0.00036, params: {'reg_lambda': 0.1},\n",
       "  mean: 0.52055, std: 0.00231, params: {'reg_lambda': 1},\n",
       "  mean: 0.51697, std: 0.00169, params: {'reg_lambda': 100}],\n",
       " {'reg_lambda': 1},\n",
       " 0.52055)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search8.grid_scores_, grid_search8.best_params_, grid_search8.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 24.273953839143118 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid8_1 = {'reg_lambda':[0.9, 1, 2, 3]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search8_1 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, reg_alpha= 1e-05,\n",
    "                                                      subsample = 0.75, colsample_bytre6 = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid8_1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search8_1.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.52014, std: 0.00073, params: {'reg_lambda': 0.9},\n",
       "  mean: 0.52055, std: 0.00231, params: {'reg_lambda': 1},\n",
       "  mean: 0.51859, std: 0.00069, params: {'reg_lambda': 2},\n",
       "  mean: 0.51884, std: 0.00058, params: {'reg_lambda': 3}],\n",
       " {'reg_lambda': 1},\n",
       " 0.52055)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search8_1.grid_scores_, grid_search8_1.best_params_, grid_search8_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune learning_rate \n",
    "guide: https://machinelearningmastery.com/tune-learning-rate-for-gradient-boosting-with-xgboost-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 35.043123706181845 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid9 = {'learning_rate': [1e-4, 0.1e-3, 1e-2, 1e-1, 0.2, 0.3]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search9 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, reg_alpha= 1e-05,\n",
    "                                                      subsample = 0.75, colsample_bytre6 = 0.85, reg_lambda= 1,\n",
    "                                                      objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid9, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search9.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50733, std: 0.00145, params: {'learning_rate': 0.0001},\n",
       "  mean: 0.50733, std: 0.00145, params: {'learning_rate': 0.0001},\n",
       "  mean: 0.51307, std: 0.00066, params: {'learning_rate': 0.01},\n",
       "  mean: 0.52055, std: 0.00231, params: {'learning_rate': 0.1},\n",
       "  mean: 0.51499, std: 0.00048, params: {'learning_rate': 0.2},\n",
       "  mean: 0.51159, std: 0.00027, params: {'learning_rate': 0.3}],\n",
       " {'learning_rate': 0.1},\n",
       " 0.52055)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search9.grid_scores_, grid_search9.best_params_, grid_search9.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train classifier with optimum hyperparameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=11,\n",
       "       min_child_weight=6, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=1e-05, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.75)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "model = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, subsample = 0.75, \n",
    "                      colsample_bytree = 0.85, reg_alpha= 1e-05, reg_lambda= 1, learning_rate = 0.1,\n",
    "                      objective='multi:softprob', num_class=8, early_stopping_rounds=20)\n",
    "\n",
    "\n",
    "eval_set = [(X_32train_new, y_32_train), (X_test1, y_test1)]\n",
    "model.fit(X_32train_new, y_32_train, eval_set = eval_set, eval_metric=[\"merror\", \"mlogloss\"], verbose = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.12275\n",
      "Test accuracy at  -18 dB = 0.119\n",
      "Test accuracy at  -16 dB = 0.12275\n",
      "Test accuracy at  -14 dB = 0.12375\n",
      "Test accuracy at  -12 dB = 0.15075\n",
      "Test accuracy at  -10 dB = 0.1745\n",
      "Test accuracy at  -8 dB = 0.278\n",
      "Test accuracy at  -6 dB = 0.3545\n",
      "Test accuracy at  -4 dB = 0.40225\n",
      "Test accuracy at  -2 dB = 0.474\n",
      "Test accuracy at  0 dB = 0.58575\n",
      "Test accuracy at  2 dB = 0.7\n",
      "Test accuracy at  4 dB = 0.8275\n",
      "Test accuracy at  6 dB = 0.85275\n",
      "Test accuracy at  8 dB = 0.8555\n",
      "Test accuracy at  10 dB = 0.85975\n",
      "Test accuracy at  12 dB = 0.8585\n",
      "Test accuracy at  14 dB = 0.86175\n",
      "Test accuracy at  16 dB = 0.85775\n",
      "Test accuracy at  18 dB = 0.86525\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = model.predict(X_32test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_32_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <bound method Booster.__del__ of <xgboost.core.Booster object at 0x000001C32D0C2E48>>\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\users\\wiilab\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\xgboost\\core.py\", line 745, in __del__\n",
      "    if self.handle is not None:\n",
      "AttributeError: 'Booster' object has no attribute 'handle'\n"
     ]
    },
    {
     "data": {
      "image/png": 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YVs2FiFgYeT6MucR5jEzo2XMbY/qUgCUFEZkNXAJUqWrhUfZ/CfghTh9FA/AN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\nScEYY0y7/w8z7uihWvs97AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c32e6d52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NJSJLgZ6qWub2e4BFqtrdL5H6iSWL47duxwHue285c3J2kpoQzY2nteWyfhk0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\nkoUxxpga/T9nkkuWZXW/fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c334574fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results = model.evals_result()\n",
    "epochs = len(results['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QKIivg4P9PxmGFtg2UgHurrb/K1alcHcpvw3+vjLEfNmswgL1peCeiEuK1zuK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\nUdphyCUiIiKitMOQS0RERERp5y98YlB53lWK3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3345e3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.89  0.00   0.00  0.00  0.00   0.05   0.05  0.01\n",
      "BPSK   0.00  0.99   0.00  0.00  0.00   0.00   0.00  0.00\n",
      "CPFSK  0.00  0.00   0.98  0.00  0.00   0.01   0.01  0.00\n",
      "GFSK   0.01  0.00   0.00  0.98  0.00   0.00   0.01  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.97   0.01   0.00  0.00\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.56   0.43  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.42   0.57  0.00\n",
      "QPSK   0.02  0.00   0.00  0.00  0.00   0.04   0.01  0.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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gKnzg+xt60r0pPej961joPkVDr/s3sqZD+wf7+6+ur0MamNnT0MxJv8wl//9TzUkQFEII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3eimZkZdnZ27N69Gzc3N2WzjImJSZd8COoDoVqt5tSpUzzxxBOEhIQoeX76NnJycsjPzycqKqrT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\naP0fIh4b5qkZGIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33ba5c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_32_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use xbg library (rather than sklearn xgboost wrapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Form dmatrices required by xbg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_32train_new, label=y_32_train)\n",
    "dtest = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    dtest[snr] = xgb.DMatrix(X_32test_new[snr], label=y_32_test[snr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_boost_rounds = 100 = default # of estimators \n",
    "(num_boost_round = no. of trees to train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=11,\n",
       "       min_child_weight=6, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=1e-05, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.75)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbclassifier_model = model  #model whose hyperparameters we tuned\n",
    "parameters = xgbclassifier_model.get_xgb_params()\n",
    "\n",
    "#Use cross validation to set n_estimators \n",
    "eval_history = xgb.cv(parameters, dtrain, metrics = ['mlogloss', 'merror'], nfold = 5, early_stopping_rounds = 20,\n",
    "                         num_boost_round = xgbclassifier_model.get_params()['n_estimators'])\n",
    "xgbclassifier_model.set_params(n_estimators = eval_history.shape[0])\n",
    "\n",
    "xgbclassifier_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:1.94242\tvalidation_0-merror:0.49605\tvalidation_1-mlogloss:1.94496\tvalidation_1-merror:0.5125\n",
      "[1]\tvalidation_0-mlogloss:1.84592\tvalidation_0-merror:0.487038\tvalidation_1-mlogloss:1.85107\tvalidation_1-merror:0.50645\n",
      "[2]\tvalidation_0-mlogloss:1.76645\tvalidation_0-merror:0.481887\tvalidation_1-mlogloss:1.77405\tvalidation_1-merror:0.505188\n",
      "[3]\tvalidation_0-mlogloss:1.70012\tvalidation_0-merror:0.4801\tvalidation_1-mlogloss:1.70999\tvalidation_1-merror:0.504613\n",
      "[4]\tvalidation_0-mlogloss:1.64396\tvalidation_0-merror:0.478\tvalidation_1-mlogloss:1.65611\tvalidation_1-merror:0.503012\n",
      "[5]\tvalidation_0-mlogloss:1.59659\tvalidation_0-merror:0.4765\tvalidation_1-mlogloss:1.61064\tvalidation_1-merror:0.503287\n",
      "[6]\tvalidation_0-mlogloss:1.55416\tvalidation_0-merror:0.476088\tvalidation_1-mlogloss:1.57024\tvalidation_1-merror:0.502612\n",
      "[7]\tvalidation_0-mlogloss:1.5172\tvalidation_0-merror:0.474625\tvalidation_1-mlogloss:1.53524\tvalidation_1-merror:0.50195\n",
      "[8]\tvalidation_0-mlogloss:1.48358\tvalidation_0-merror:0.472338\tvalidation_1-mlogloss:1.50362\tvalidation_1-merror:0.500888\n",
      "[9]\tvalidation_0-mlogloss:1.45402\tvalidation_0-merror:0.47225\tvalidation_1-mlogloss:1.47588\tvalidation_1-merror:0.500825\n",
      "[10]\tvalidation_0-mlogloss:1.42713\tvalidation_0-merror:0.469737\tvalidation_1-mlogloss:1.45076\tvalidation_1-merror:0.499038\n",
      "[11]\tvalidation_0-mlogloss:1.40484\tvalidation_0-merror:0.468087\tvalidation_1-mlogloss:1.43017\tvalidation_1-merror:0.498825\n",
      "[12]\tvalidation_0-mlogloss:1.38281\tvalidation_0-merror:0.466487\tvalidation_1-mlogloss:1.41001\tvalidation_1-merror:0.497963\n",
      "[13]\tvalidation_0-mlogloss:1.36315\tvalidation_0-merror:0.465138\tvalidation_1-mlogloss:1.39211\tvalidation_1-merror:0.497725\n",
      "[14]\tvalidation_0-mlogloss:1.34591\tvalidation_0-merror:0.463625\tvalidation_1-mlogloss:1.37644\tvalidation_1-merror:0.497162\n",
      "[15]\tvalidation_0-mlogloss:1.32951\tvalidation_0-merror:0.46225\tvalidation_1-mlogloss:1.36166\tvalidation_1-merror:0.497238\n",
      "[16]\tvalidation_0-mlogloss:1.31396\tvalidation_0-merror:0.460063\tvalidation_1-mlogloss:1.34764\tvalidation_1-merror:0.49655\n",
      "[17]\tvalidation_0-mlogloss:1.30027\tvalidation_0-merror:0.459575\tvalidation_1-mlogloss:1.33535\tvalidation_1-merror:0.496\n",
      "[18]\tvalidation_0-mlogloss:1.28854\tvalidation_0-merror:0.458575\tvalidation_1-mlogloss:1.32499\tvalidation_1-merror:0.495775\n",
      "[19]\tvalidation_0-mlogloss:1.27675\tvalidation_0-merror:0.457687\tvalidation_1-mlogloss:1.31444\tvalidation_1-merror:0.49505\n",
      "[20]\tvalidation_0-mlogloss:1.26637\tvalidation_0-merror:0.456863\tvalidation_1-mlogloss:1.30532\tvalidation_1-merror:0.494287\n",
      "[21]\tvalidation_0-mlogloss:1.25644\tvalidation_0-merror:0.455638\tvalidation_1-mlogloss:1.29684\tvalidation_1-merror:0.494063\n",
      "[22]\tvalidation_0-mlogloss:1.24692\tvalidation_0-merror:0.454725\tvalidation_1-mlogloss:1.2887\tvalidation_1-merror:0.4946\n",
      "[23]\tvalidation_0-mlogloss:1.23835\tvalidation_0-merror:0.454125\tvalidation_1-mlogloss:1.28147\tvalidation_1-merror:0.494112\n",
      "[24]\tvalidation_0-mlogloss:1.23045\tvalidation_0-merror:0.453512\tvalidation_1-mlogloss:1.27487\tvalidation_1-merror:0.494013\n",
      "[25]\tvalidation_0-mlogloss:1.22314\tvalidation_0-merror:0.451425\tvalidation_1-mlogloss:1.26868\tvalidation_1-merror:0.493762\n",
      "[26]\tvalidation_0-mlogloss:1.21628\tvalidation_0-merror:0.45105\tvalidation_1-mlogloss:1.26309\tvalidation_1-merror:0.493275\n",
      "[27]\tvalidation_0-mlogloss:1.20994\tvalidation_0-merror:0.450588\tvalidation_1-mlogloss:1.25798\tvalidation_1-merror:0.493612\n",
      "[28]\tvalidation_0-mlogloss:1.20411\tvalidation_0-merror:0.449125\tvalidation_1-mlogloss:1.25334\tvalidation_1-merror:0.493612\n",
      "[29]\tvalidation_0-mlogloss:1.19847\tvalidation_0-merror:0.44785\tvalidation_1-mlogloss:1.24887\tvalidation_1-merror:0.493113\n",
      "[30]\tvalidation_0-mlogloss:1.19343\tvalidation_0-merror:0.447037\tvalidation_1-mlogloss:1.24506\tvalidation_1-merror:0.492825\n",
      "[31]\tvalidation_0-mlogloss:1.18859\tvalidation_0-merror:0.445962\tvalidation_1-mlogloss:1.24134\tvalidation_1-merror:0.492887\n",
      "[32]\tvalidation_0-mlogloss:1.18381\tvalidation_0-merror:0.445225\tvalidation_1-mlogloss:1.23771\tvalidation_1-merror:0.49275\n",
      "[33]\tvalidation_0-mlogloss:1.1793\tvalidation_0-merror:0.443812\tvalidation_1-mlogloss:1.23447\tvalidation_1-merror:0.493012\n",
      "[34]\tvalidation_0-mlogloss:1.17527\tvalidation_0-merror:0.443087\tvalidation_1-mlogloss:1.23152\tvalidation_1-merror:0.49275\n",
      "[35]\tvalidation_0-mlogloss:1.1716\tvalidation_0-merror:0.442425\tvalidation_1-mlogloss:1.22881\tvalidation_1-merror:0.491875\n",
      "[36]\tvalidation_0-mlogloss:1.16761\tvalidation_0-merror:0.44175\tvalidation_1-mlogloss:1.22611\tvalidation_1-merror:0.492088\n",
      "[37]\tvalidation_0-mlogloss:1.16422\tvalidation_0-merror:0.441475\tvalidation_1-mlogloss:1.22361\tvalidation_1-merror:0.491812\n",
      "[38]\tvalidation_0-mlogloss:1.16078\tvalidation_0-merror:0.441213\tvalidation_1-mlogloss:1.22114\tvalidation_1-merror:0.491275\n",
      "[39]\tvalidation_0-mlogloss:1.15736\tvalidation_0-merror:0.4404\tvalidation_1-mlogloss:1.21871\tvalidation_1-merror:0.491462\n",
      "[40]\tvalidation_0-mlogloss:1.15444\tvalidation_0-merror:0.439512\tvalidation_1-mlogloss:1.21675\tvalidation_1-merror:0.49135\n",
      "[41]\tvalidation_0-mlogloss:1.15165\tvalidation_0-merror:0.43915\tvalidation_1-mlogloss:1.2149\tvalidation_1-merror:0.491575\n",
      "[42]\tvalidation_0-mlogloss:1.14887\tvalidation_0-merror:0.438587\tvalidation_1-mlogloss:1.21314\tvalidation_1-merror:0.491125\n",
      "[43]\tvalidation_0-mlogloss:1.14626\tvalidation_0-merror:0.437675\tvalidation_1-mlogloss:1.21139\tvalidation_1-merror:0.4911\n",
      "[44]\tvalidation_0-mlogloss:1.14382\tvalidation_0-merror:0.436325\tvalidation_1-mlogloss:1.20992\tvalidation_1-merror:0.490575\n",
      "[45]\tvalidation_0-mlogloss:1.14141\tvalidation_0-merror:0.43535\tvalidation_1-mlogloss:1.20862\tvalidation_1-merror:0.491025\n",
      "[46]\tvalidation_0-mlogloss:1.13908\tvalidation_0-merror:0.433863\tvalidation_1-mlogloss:1.20727\tvalidation_1-merror:0.490687\n",
      "[47]\tvalidation_0-mlogloss:1.13711\tvalidation_0-merror:0.432175\tvalidation_1-mlogloss:1.2062\tvalidation_1-merror:0.490925\n",
      "[48]\tvalidation_0-mlogloss:1.13467\tvalidation_0-merror:0.431175\tvalidation_1-mlogloss:1.20498\tvalidation_1-merror:0.490862\n",
      "[49]\tvalidation_0-mlogloss:1.13241\tvalidation_0-merror:0.430737\tvalidation_1-mlogloss:1.20371\tvalidation_1-merror:0.490537\n",
      "[50]\tvalidation_0-mlogloss:1.13006\tvalidation_0-merror:0.428962\tvalidation_1-mlogloss:1.20254\tvalidation_1-merror:0.489962\n",
      "[51]\tvalidation_0-mlogloss:1.12784\tvalidation_0-merror:0.42825\tvalidation_1-mlogloss:1.20135\tvalidation_1-merror:0.4898\n",
      "[52]\tvalidation_0-mlogloss:1.12589\tvalidation_0-merror:0.426825\tvalidation_1-mlogloss:1.20049\tvalidation_1-merror:0.490012\n",
      "[53]\tvalidation_0-mlogloss:1.12395\tvalidation_0-merror:0.42585\tvalidation_1-mlogloss:1.19952\tvalidation_1-merror:0.490263\n",
      "[54]\tvalidation_0-mlogloss:1.12207\tvalidation_0-merror:0.425425\tvalidation_1-mlogloss:1.19858\tvalidation_1-merror:0.49025\n",
      "[55]\tvalidation_0-mlogloss:1.12018\tvalidation_0-merror:0.424462\tvalidation_1-mlogloss:1.19776\tvalidation_1-merror:0.49005\n",
      "[56]\tvalidation_0-mlogloss:1.11831\tvalidation_0-merror:0.423113\tvalidation_1-mlogloss:1.19684\tvalidation_1-merror:0.489713\n",
      "[57]\tvalidation_0-mlogloss:1.11677\tvalidation_0-merror:0.421762\tvalidation_1-mlogloss:1.19623\tvalidation_1-merror:0.489863\n",
      "[58]\tvalidation_0-mlogloss:1.11469\tvalidation_0-merror:0.4207\tvalidation_1-mlogloss:1.19553\tvalidation_1-merror:0.48955\n",
      "[59]\tvalidation_0-mlogloss:1.11311\tvalidation_0-merror:0.420087\tvalidation_1-mlogloss:1.19488\tvalidation_1-merror:0.489437\n",
      "[60]\tvalidation_0-mlogloss:1.11132\tvalidation_0-merror:0.418375\tvalidation_1-mlogloss:1.19436\tvalidation_1-merror:0.488512\n",
      "[61]\tvalidation_0-mlogloss:1.10988\tvalidation_0-merror:0.417412\tvalidation_1-mlogloss:1.19379\tvalidation_1-merror:0.488475\n",
      "[62]\tvalidation_0-mlogloss:1.10836\tvalidation_0-merror:0.416663\tvalidation_1-mlogloss:1.19322\tvalidation_1-merror:0.488313\n",
      "[63]\tvalidation_0-mlogloss:1.10674\tvalidation_0-merror:0.415237\tvalidation_1-mlogloss:1.19279\tvalidation_1-merror:0.48875\n",
      "[64]\tvalidation_0-mlogloss:1.10518\tvalidation_0-merror:0.4143\tvalidation_1-mlogloss:1.19229\tvalidation_1-merror:0.4886\n",
      "[65]\tvalidation_0-mlogloss:1.10342\tvalidation_0-merror:0.41335\tvalidation_1-mlogloss:1.19182\tvalidation_1-merror:0.489038\n",
      "[66]\tvalidation_0-mlogloss:1.10182\tvalidation_0-merror:0.411887\tvalidation_1-mlogloss:1.19143\tvalidation_1-merror:0.489388\n",
      "[67]\tvalidation_0-mlogloss:1.10028\tvalidation_0-merror:0.410925\tvalidation_1-mlogloss:1.19103\tvalidation_1-merror:0.489025\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68]\tvalidation_0-mlogloss:1.09912\tvalidation_0-merror:0.410363\tvalidation_1-mlogloss:1.1907\tvalidation_1-merror:0.488687\n",
      "[69]\tvalidation_0-mlogloss:1.09788\tvalidation_0-merror:0.409362\tvalidation_1-mlogloss:1.19039\tvalidation_1-merror:0.488663\n",
      "[70]\tvalidation_0-mlogloss:1.09665\tvalidation_0-merror:0.409038\tvalidation_1-mlogloss:1.18992\tvalidation_1-merror:0.488613\n",
      "[71]\tvalidation_0-mlogloss:1.09513\tvalidation_0-merror:0.408237\tvalidation_1-mlogloss:1.18957\tvalidation_1-merror:0.488963\n",
      "[72]\tvalidation_0-mlogloss:1.09371\tvalidation_0-merror:0.407063\tvalidation_1-mlogloss:1.18925\tvalidation_1-merror:0.4891\n",
      "[73]\tvalidation_0-mlogloss:1.09244\tvalidation_0-merror:0.406\tvalidation_1-mlogloss:1.18897\tvalidation_1-merror:0.489062\n",
      "[74]\tvalidation_0-mlogloss:1.09063\tvalidation_0-merror:0.403488\tvalidation_1-mlogloss:1.18876\tvalidation_1-merror:0.489013\n",
      "[75]\tvalidation_0-mlogloss:1.0894\tvalidation_0-merror:0.402487\tvalidation_1-mlogloss:1.18851\tvalidation_1-merror:0.488525\n",
      "[76]\tvalidation_0-mlogloss:1.08797\tvalidation_0-merror:0.400862\tvalidation_1-mlogloss:1.18833\tvalidation_1-merror:0.488825\n",
      "[77]\tvalidation_0-mlogloss:1.08689\tvalidation_0-merror:0.399763\tvalidation_1-mlogloss:1.18818\tvalidation_1-merror:0.4889\n",
      "[78]\tvalidation_0-mlogloss:1.08543\tvalidation_0-merror:0.3981\tvalidation_1-mlogloss:1.18798\tvalidation_1-merror:0.489138\n",
      "[79]\tvalidation_0-mlogloss:1.08444\tvalidation_0-merror:0.3973\tvalidation_1-mlogloss:1.18777\tvalidation_1-merror:0.489312\n",
      "[80]\tvalidation_0-mlogloss:1.08301\tvalidation_0-merror:0.39575\tvalidation_1-mlogloss:1.18749\tvalidation_1-merror:0.4891\n",
      "[81]\tvalidation_0-mlogloss:1.08193\tvalidation_0-merror:0.394937\tvalidation_1-mlogloss:1.18725\tvalidation_1-merror:0.489062\n",
      "[82]\tvalidation_0-mlogloss:1.08092\tvalidation_0-merror:0.394662\tvalidation_1-mlogloss:1.18715\tvalidation_1-merror:0.488975\n",
      "[83]\tvalidation_0-mlogloss:1.07949\tvalidation_0-merror:0.392612\tvalidation_1-mlogloss:1.18699\tvalidation_1-merror:0.489563\n",
      "[84]\tvalidation_0-mlogloss:1.07814\tvalidation_0-merror:0.39125\tvalidation_1-mlogloss:1.18672\tvalidation_1-merror:0.489163\n",
      "[85]\tvalidation_0-mlogloss:1.07705\tvalidation_0-merror:0.390588\tvalidation_1-mlogloss:1.18658\tvalidation_1-merror:0.489175\n",
      "[86]\tvalidation_0-mlogloss:1.07614\tvalidation_0-merror:0.38955\tvalidation_1-mlogloss:1.18651\tvalidation_1-merror:0.489375\n",
      "[87]\tvalidation_0-mlogloss:1.07517\tvalidation_0-merror:0.388675\tvalidation_1-mlogloss:1.18643\tvalidation_1-merror:0.489475\n",
      "[88]\tvalidation_0-mlogloss:1.07395\tvalidation_0-merror:0.388088\tvalidation_1-mlogloss:1.18627\tvalidation_1-merror:0.48945\n",
      "[89]\tvalidation_0-mlogloss:1.07269\tvalidation_0-merror:0.387575\tvalidation_1-mlogloss:1.18616\tvalidation_1-merror:0.489275\n",
      "[90]\tvalidation_0-mlogloss:1.07143\tvalidation_0-merror:0.38625\tvalidation_1-mlogloss:1.18599\tvalidation_1-merror:0.48915\n",
      "[91]\tvalidation_0-mlogloss:1.07021\tvalidation_0-merror:0.384887\tvalidation_1-mlogloss:1.18589\tvalidation_1-merror:0.489225\n",
      "[92]\tvalidation_0-mlogloss:1.06905\tvalidation_0-merror:0.38335\tvalidation_1-mlogloss:1.18577\tvalidation_1-merror:0.4893\n",
      "[93]\tvalidation_0-mlogloss:1.0677\tvalidation_0-merror:0.381425\tvalidation_1-mlogloss:1.1856\tvalidation_1-merror:0.489013\n",
      "[94]\tvalidation_0-mlogloss:1.06641\tvalidation_0-merror:0.380313\tvalidation_1-mlogloss:1.18556\tvalidation_1-merror:0.488975\n",
      "[95]\tvalidation_0-mlogloss:1.06513\tvalidation_0-merror:0.378888\tvalidation_1-mlogloss:1.1854\tvalidation_1-merror:0.4887\n",
      "[96]\tvalidation_0-mlogloss:1.06402\tvalidation_0-merror:0.377212\tvalidation_1-mlogloss:1.18541\tvalidation_1-merror:0.489038\n",
      "[97]\tvalidation_0-mlogloss:1.06204\tvalidation_0-merror:0.374512\tvalidation_1-mlogloss:1.18523\tvalidation_1-merror:0.488788\n",
      "[98]\tvalidation_0-mlogloss:1.06113\tvalidation_0-merror:0.373937\tvalidation_1-mlogloss:1.18524\tvalidation_1-merror:0.488712\n",
      "[99]\tvalidation_0-mlogloss:1.05985\tvalidation_0-merror:0.372613\tvalidation_1-mlogloss:1.18519\tvalidation_1-merror:0.48895\n",
      "Fitting model to data took 2.981314512093862 minutes\n"
     ]
    }
   ],
   "source": [
    "eval_set = [(X_32train_new, y_32_train), (X_test1, y_test1)]\n",
    "start = time()\n",
    "xgbclassifier_model.fit(X_32train_new, y_32_train, eval_metric = ['mlogloss','merror'], \n",
    "                        eval_set = eval_set) #fit model to data\n",
    "print(\"Fitting model to data took {} minutes\".format((time() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gWLthDG0zlGHthtEtrZt1fhtTTVRFkvhBVXsfad1h9m8HvFNekihTriGwWFXL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\nxoRkScIYY0xI/x/7oiMFueWmWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3345c9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EEyZALJbuyEQk2VIxdlOxu9dubl6yKUlsvaam8GyLK68MHfWOPhqOPTb87KXu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\nISIiCSlJiIhIQv8f6eIzZCvpcFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33a23c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results_xgbclassifier = xgbclassifier_model.evals_result()\n",
    "epochs = len(results_xgbclassifier['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.12275\n",
      "Test accuracy at  -18 dB = 0.119\n",
      "Test accuracy at  -16 dB = 0.12275\n",
      "Test accuracy at  -14 dB = 0.12375\n",
      "Test accuracy at  -12 dB = 0.15075\n",
      "Test accuracy at  -10 dB = 0.1745\n",
      "Test accuracy at  -8 dB = 0.278\n",
      "Test accuracy at  -6 dB = 0.3545\n",
      "Test accuracy at  -4 dB = 0.40225\n",
      "Test accuracy at  -2 dB = 0.474\n",
      "Test accuracy at  0 dB = 0.58575\n",
      "Test accuracy at  2 dB = 0.7\n",
      "Test accuracy at  4 dB = 0.8275\n",
      "Test accuracy at  6 dB = 0.85275\n",
      "Test accuracy at  8 dB = 0.8555\n",
      "Test accuracy at  10 dB = 0.85975\n",
      "Test accuracy at  12 dB = 0.8585\n",
      "Test accuracy at  14 dB = 0.86175\n",
      "Test accuracy at  16 dB = 0.85775\n",
      "Test accuracy at  18 dB = 0.86525\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = xgbclassifier_model.predict(X_32test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_32_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QKIivg4P9PxmGFtg2UgHurrb/K1alcHcpvw3+vjLEfNmswgL1peCeiEuK1zuK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\nUdphyCUiIiKitMOQS0RERERp5y98YlB53lWK3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c32d0f48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.89  0.00   0.00  0.00  0.00   0.05   0.05  0.01\n",
      "BPSK   0.00  0.99   0.00  0.00  0.00   0.00   0.00  0.00\n",
      "CPFSK  0.00  0.00   0.98  0.00  0.00   0.01   0.01  0.00\n",
      "GFSK   0.01  0.00   0.00  0.98  0.00   0.00   0.01  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.97   0.01   0.00  0.00\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.56   0.43  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.42   0.57  0.00\n",
      "QPSK   0.02  0.00   0.00  0.00  0.00   0.04   0.01  0.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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gKnzg+xt60r0pPej961joPkVDr/s3sqZD+wf7+6+ur0MamNnT0MxJv8wl//9TzUkQFEII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3eimZkZdnZ27N69Gzc3N2WzjImJSZd8COoDoVqt5tSpUzzxxBOEhIQoeX76NnJycsjPzycqKqrT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\naP0fIh4b5qkZGIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33de34ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_32_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### num_boost_round = 300; n_estimators = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:1.92755\tvalidation_0-merror:0.462388\tvalidation_1-mlogloss:1.93455\tvalidation_1-merror:0.502\n",
      "[1]\tvalidation_0-mlogloss:1.81628\tvalidation_0-merror:0.437212\tvalidation_1-mlogloss:1.83036\tvalidation_1-merror:0.49475\n",
      "[2]\tvalidation_0-mlogloss:1.73125\tvalidation_0-merror:0.42695\tvalidation_1-mlogloss:1.75192\tvalidation_1-merror:0.492038\n",
      "[3]\tvalidation_0-mlogloss:1.65796\tvalidation_0-merror:0.416537\tvalidation_1-mlogloss:1.68513\tvalidation_1-merror:0.49\n",
      "[4]\tvalidation_0-mlogloss:1.59742\tvalidation_0-merror:0.4087\tvalidation_1-mlogloss:1.6311\tvalidation_1-merror:0.488463\n",
      "[5]\tvalidation_0-mlogloss:1.54234\tvalidation_0-merror:0.401038\tvalidation_1-mlogloss:1.58221\tvalidation_1-merror:0.488512\n",
      "[6]\tvalidation_0-mlogloss:1.49356\tvalidation_0-merror:0.394675\tvalidation_1-mlogloss:1.53902\tvalidation_1-merror:0.487775\n",
      "[7]\tvalidation_0-mlogloss:1.45166\tvalidation_0-merror:0.390763\tvalidation_1-mlogloss:1.5023\tvalidation_1-merror:0.486688\n",
      "[8]\tvalidation_0-mlogloss:1.41387\tvalidation_0-merror:0.387087\tvalidation_1-mlogloss:1.46948\tvalidation_1-merror:0.485525\n",
      "[9]\tvalidation_0-mlogloss:1.3803\tvalidation_0-merror:0.383738\tvalidation_1-mlogloss:1.44108\tvalidation_1-merror:0.485537\n",
      "[10]\tvalidation_0-mlogloss:1.34935\tvalidation_0-merror:0.379137\tvalidation_1-mlogloss:1.41492\tvalidation_1-merror:0.485275\n",
      "[11]\tvalidation_0-mlogloss:1.32144\tvalidation_0-merror:0.376588\tvalidation_1-mlogloss:1.39164\tvalidation_1-merror:0.484787\n",
      "[12]\tvalidation_0-mlogloss:1.29497\tvalidation_0-merror:0.371563\tvalidation_1-mlogloss:1.37049\tvalidation_1-merror:0.483525\n",
      "[13]\tvalidation_0-mlogloss:1.27129\tvalidation_0-merror:0.368638\tvalidation_1-mlogloss:1.3514\tvalidation_1-merror:0.48345\n",
      "[14]\tvalidation_0-mlogloss:1.24947\tvalidation_0-merror:0.364763\tvalidation_1-mlogloss:1.33387\tvalidation_1-merror:0.482287\n",
      "[15]\tvalidation_0-mlogloss:1.22945\tvalidation_0-merror:0.361463\tvalidation_1-mlogloss:1.31827\tvalidation_1-merror:0.482363\n",
      "[16]\tvalidation_0-mlogloss:1.21045\tvalidation_0-merror:0.357837\tvalidation_1-mlogloss:1.30389\tvalidation_1-merror:0.482112\n",
      "[17]\tvalidation_0-mlogloss:1.19347\tvalidation_0-merror:0.354275\tvalidation_1-mlogloss:1.29122\tvalidation_1-merror:0.482025\n",
      "[18]\tvalidation_0-mlogloss:1.17767\tvalidation_0-merror:0.351912\tvalidation_1-mlogloss:1.27961\tvalidation_1-merror:0.48195\n",
      "[19]\tvalidation_0-mlogloss:1.16276\tvalidation_0-merror:0.348538\tvalidation_1-mlogloss:1.26891\tvalidation_1-merror:0.481313\n",
      "[20]\tvalidation_0-mlogloss:1.14904\tvalidation_0-merror:0.344875\tvalidation_1-mlogloss:1.25901\tvalidation_1-merror:0.481313\n",
      "[21]\tvalidation_0-mlogloss:1.13658\tvalidation_0-merror:0.342337\tvalidation_1-mlogloss:1.25021\tvalidation_1-merror:0.480888\n",
      "[22]\tvalidation_0-mlogloss:1.12466\tvalidation_0-merror:0.338962\tvalidation_1-mlogloss:1.24213\tvalidation_1-merror:0.479975\n",
      "[23]\tvalidation_0-mlogloss:1.11364\tvalidation_0-merror:0.336287\tvalidation_1-mlogloss:1.23463\tvalidation_1-merror:0.479638\n",
      "[24]\tvalidation_0-mlogloss:1.10365\tvalidation_0-merror:0.333975\tvalidation_1-mlogloss:1.22778\tvalidation_1-merror:0.480225\n",
      "[25]\tvalidation_0-mlogloss:1.09374\tvalidation_0-merror:0.3307\tvalidation_1-mlogloss:1.22177\tvalidation_1-merror:0.479725\n",
      "[26]\tvalidation_0-mlogloss:1.08488\tvalidation_0-merror:0.328687\tvalidation_1-mlogloss:1.21621\tvalidation_1-merror:0.480238\n",
      "[27]\tvalidation_0-mlogloss:1.07592\tvalidation_0-merror:0.325625\tvalidation_1-mlogloss:1.21107\tvalidation_1-merror:0.479875\n",
      "[28]\tvalidation_0-mlogloss:1.06768\tvalidation_0-merror:0.323388\tvalidation_1-mlogloss:1.20637\tvalidation_1-merror:0.47955\n",
      "[29]\tvalidation_0-mlogloss:1.06026\tvalidation_0-merror:0.321537\tvalidation_1-mlogloss:1.20197\tvalidation_1-merror:0.479212\n",
      "[30]\tvalidation_0-mlogloss:1.05309\tvalidation_0-merror:0.319012\tvalidation_1-mlogloss:1.19775\tvalidation_1-merror:0.478487\n",
      "[31]\tvalidation_0-mlogloss:1.04565\tvalidation_0-merror:0.3156\tvalidation_1-mlogloss:1.19373\tvalidation_1-merror:0.478137\n",
      "[32]\tvalidation_0-mlogloss:1.03943\tvalidation_0-merror:0.313738\tvalidation_1-mlogloss:1.19025\tvalidation_1-merror:0.478013\n",
      "[33]\tvalidation_0-mlogloss:1.03307\tvalidation_0-merror:0.3108\tvalidation_1-mlogloss:1.18712\tvalidation_1-merror:0.477538\n",
      "[34]\tvalidation_0-mlogloss:1.02753\tvalidation_0-merror:0.309125\tvalidation_1-mlogloss:1.18412\tvalidation_1-merror:0.477625\n",
      "[35]\tvalidation_0-mlogloss:1.02178\tvalidation_0-merror:0.306637\tvalidation_1-mlogloss:1.18144\tvalidation_1-merror:0.477025\n",
      "[36]\tvalidation_0-mlogloss:1.0165\tvalidation_0-merror:0.305438\tvalidation_1-mlogloss:1.17884\tvalidation_1-merror:0.476963\n",
      "[37]\tvalidation_0-mlogloss:1.01197\tvalidation_0-merror:0.303863\tvalidation_1-mlogloss:1.1766\tvalidation_1-merror:0.477387\n",
      "[38]\tvalidation_0-mlogloss:1.00726\tvalidation_0-merror:0.301688\tvalidation_1-mlogloss:1.17435\tvalidation_1-merror:0.477812\n",
      "[39]\tvalidation_0-mlogloss:1.00198\tvalidation_0-merror:0.298875\tvalidation_1-mlogloss:1.17234\tvalidation_1-merror:0.4779\n",
      "[40]\tvalidation_0-mlogloss:0.997561\tvalidation_0-merror:0.296762\tvalidation_1-mlogloss:1.17037\tvalidation_1-merror:0.478\n",
      "[41]\tvalidation_0-mlogloss:0.99324\tvalidation_0-merror:0.295312\tvalidation_1-mlogloss:1.16868\tvalidation_1-merror:0.477525\n",
      "[42]\tvalidation_0-mlogloss:0.988746\tvalidation_0-merror:0.2938\tvalidation_1-mlogloss:1.16709\tvalidation_1-merror:0.4775\n",
      "[43]\tvalidation_0-mlogloss:0.983837\tvalidation_0-merror:0.290713\tvalidation_1-mlogloss:1.16555\tvalidation_1-merror:0.477163\n",
      "[44]\tvalidation_0-mlogloss:0.979391\tvalidation_0-merror:0.2877\tvalidation_1-mlogloss:1.16409\tvalidation_1-merror:0.477262\n",
      "[45]\tvalidation_0-mlogloss:0.975677\tvalidation_0-merror:0.286162\tvalidation_1-mlogloss:1.16283\tvalidation_1-merror:0.476525\n",
      "[46]\tvalidation_0-mlogloss:0.971799\tvalidation_0-merror:0.283688\tvalidation_1-mlogloss:1.1616\tvalidation_1-merror:0.477075\n",
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      "[296]\tvalidation_0-mlogloss:0.358631\tvalidation_0-merror:0.002725\tvalidation_1-mlogloss:1.17198\tvalidation_1-merror:0.477488\n",
      "[297]\tvalidation_0-mlogloss:0.357121\tvalidation_0-merror:0.002688\tvalidation_1-mlogloss:1.17214\tvalidation_1-merror:0.477737\n",
      "[298]\tvalidation_0-mlogloss:0.355903\tvalidation_0-merror:0.00265\tvalidation_1-mlogloss:1.17231\tvalidation_1-merror:0.47775\n",
      "[299]\tvalidation_0-mlogloss:0.354068\tvalidation_0-merror:0.002475\tvalidation_1-mlogloss:1.1725\tvalidation_1-merror:0.477688\n",
      "Cross validation and fitting took 52.6905723174413 minutes\n"
     ]
    }
   ],
   "source": [
    "xgbclassifier_model2 = model  #model whose hyperparameters we tuned\n",
    "parameters = xgbclassifier_model2.get_xgb_params()\n",
    "\n",
    "#Use cross validation to set n_estimators\n",
    "start = time()\n",
    "eval_history = xgb.cv(parameters, dtrain, metrics = ['mlogloss', 'merror'], nfold = 5, early_stopping_rounds = 20,\n",
    "                         num_boost_round = 300)\n",
    "xgbclassifier_model2.set_params(n_estimators = 300)\n",
    "\n",
    "eval_set = [(X_32train_new, y_32_train), (X_test1, y_test1)]\n",
    "\n",
    "xgbclassifier_model2.fit(X_32train_new, y_32_train, eval_metric = ['mlogloss','merror'], \n",
    "                        eval_set = eval_set) #fit model to data\n",
    "print(\"Cross validation and fitting took {} minutes\".format((time() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test-merror-mean</th>\n",
       "      <th>test-merror-std</th>\n",
       "      <th>test-mlogloss-mean</th>\n",
       "      <th>test-mlogloss-std</th>\n",
       "      <th>train-merror-mean</th>\n",
       "      <th>train-merror-std</th>\n",
       "      <th>train-mlogloss-mean</th>\n",
       "      <th>train-mlogloss-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.507662</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>1.936791</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.458653</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>1.928032</td>\n",
       "      <td>0.000488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.501650</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>1.835599</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.435650</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>1.818443</td>\n",
       "      <td>0.002313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.497200</td>\n",
       "      <td>0.003735</td>\n",
       "      <td>1.755325</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.420594</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>1.730046</td>\n",
       "      <td>0.001781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493800</td>\n",
       "      <td>0.003169</td>\n",
       "      <td>1.688766</td>\n",
       "      <td>0.005256</td>\n",
       "      <td>0.410869</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>1.655890</td>\n",
       "      <td>0.001782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.493750</td>\n",
       "      <td>0.004102</td>\n",
       "      <td>1.633064</td>\n",
       "      <td>0.005431</td>\n",
       "      <td>0.401909</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>1.593027</td>\n",
       "      <td>0.001410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.492362</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>1.585748</td>\n",
       "      <td>0.005915</td>\n",
       "      <td>0.395597</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>1.538604</td>\n",
       "      <td>0.001142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.491350</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>1.544547</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>0.389137</td>\n",
       "      <td>0.001178</td>\n",
       "      <td>1.490676</td>\n",
       "      <td>0.001523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.491250</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>1.507688</td>\n",
       "      <td>0.006917</td>\n",
       "      <td>0.382791</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>1.447380</td>\n",
       "      <td>0.001344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.489800</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>1.474918</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>0.377150</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>1.408386</td>\n",
       "      <td>0.001138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.488650</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>1.445872</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.372106</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>1.373230</td>\n",
       "      <td>0.000783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.487988</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>1.420126</td>\n",
       "      <td>0.007252</td>\n",
       "      <td>0.367519</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>1.341698</td>\n",
       "      <td>0.001280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.486950</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>1.396999</td>\n",
       "      <td>0.007256</td>\n",
       "      <td>0.362631</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.312758</td>\n",
       "      <td>0.001003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.487225</td>\n",
       "      <td>0.003375</td>\n",
       "      <td>1.376303</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.358731</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>1.286339</td>\n",
       "      <td>0.001164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.486838</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>1.357715</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.354753</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>1.262517</td>\n",
       "      <td>0.001068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.485750</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>1.340694</td>\n",
       "      <td>0.007626</td>\n",
       "      <td>0.351091</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>1.240523</td>\n",
       "      <td>0.001233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.485687</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>1.325375</td>\n",
       "      <td>0.007811</td>\n",
       "      <td>0.347228</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>1.220359</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.485000</td>\n",
       "      <td>0.003336</td>\n",
       "      <td>1.311447</td>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.343566</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>1.201379</td>\n",
       "      <td>0.001539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.485000</td>\n",
       "      <td>0.003549</td>\n",
       "      <td>1.298823</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>0.340191</td>\n",
       "      <td>0.000664</td>\n",
       "      <td>1.184032</td>\n",
       "      <td>0.001527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.484400</td>\n",
       "      <td>0.003804</td>\n",
       "      <td>1.287546</td>\n",
       "      <td>0.007728</td>\n",
       "      <td>0.336728</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>1.168169</td>\n",
       "      <td>0.001562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.484650</td>\n",
       "      <td>0.003131</td>\n",
       "      <td>1.277022</td>\n",
       "      <td>0.007887</td>\n",
       "      <td>0.333366</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>1.152812</td>\n",
       "      <td>0.001366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.484438</td>\n",
       "      <td>0.002687</td>\n",
       "      <td>1.267365</td>\n",
       "      <td>0.007932</td>\n",
       "      <td>0.329828</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.138851</td>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.483700</td>\n",
       "      <td>0.002961</td>\n",
       "      <td>1.258529</td>\n",
       "      <td>0.007686</td>\n",
       "      <td>0.326684</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>1.125635</td>\n",
       "      <td>0.001811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.483362</td>\n",
       "      <td>0.003120</td>\n",
       "      <td>1.250384</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>0.323884</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>1.113482</td>\n",
       "      <td>0.002104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.482750</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>1.242965</td>\n",
       "      <td>0.007854</td>\n",
       "      <td>0.321428</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>1.102013</td>\n",
       "      <td>0.002112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.482437</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>1.236390</td>\n",
       "      <td>0.007895</td>\n",
       "      <td>0.319184</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>1.091539</td>\n",
       "      <td>0.002274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.482213</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>1.230182</td>\n",
       "      <td>0.007910</td>\n",
       "      <td>0.316041</td>\n",
       "      <td>0.001161</td>\n",
       "      <td>1.081177</td>\n",
       "      <td>0.002454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.482337</td>\n",
       "      <td>0.003118</td>\n",
       "      <td>1.224528</td>\n",
       "      <td>0.008014</td>\n",
       "      <td>0.313003</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>1.071584</td>\n",
       "      <td>0.002251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.482150</td>\n",
       "      <td>0.003216</td>\n",
       "      <td>1.219456</td>\n",
       "      <td>0.007941</td>\n",
       "      <td>0.310219</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>1.062853</td>\n",
       "      <td>0.002127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.481738</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>1.214718</td>\n",
       "      <td>0.007986</td>\n",
       "      <td>0.307853</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>1.054758</td>\n",
       "      <td>0.002265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.481900</td>\n",
       "      <td>0.003143</td>\n",
       "      <td>1.210479</td>\n",
       "      <td>0.007927</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>1.046835</td>\n",
       "      <td>0.002398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.480600</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>1.160830</td>\n",
       "      <td>0.008743</td>\n",
       "      <td>0.216541</td>\n",
       "      <td>0.003736</td>\n",
       "      <td>0.889058</td>\n",
       "      <td>0.004937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.480637</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>1.160431</td>\n",
       "      <td>0.008816</td>\n",
       "      <td>0.213253</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>0.884978</td>\n",
       "      <td>0.004968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.480525</td>\n",
       "      <td>0.004243</td>\n",
       "      <td>1.160080</td>\n",
       "      <td>0.008751</td>\n",
       "      <td>0.210416</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.881156</td>\n",
       "      <td>0.005173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.480375</td>\n",
       "      <td>0.003934</td>\n",
       "      <td>1.159823</td>\n",
       "      <td>0.008830</td>\n",
       "      <td>0.207244</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>0.877089</td>\n",
       "      <td>0.005135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.480300</td>\n",
       "      <td>0.003812</td>\n",
       "      <td>1.159527</td>\n",
       "      <td>0.008730</td>\n",
       "      <td>0.203916</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.873222</td>\n",
       "      <td>0.004902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.480088</td>\n",
       "      <td>0.004168</td>\n",
       "      <td>1.159245</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>0.200772</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.869109</td>\n",
       "      <td>0.005158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.480075</td>\n",
       "      <td>0.004421</td>\n",
       "      <td>1.158970</td>\n",
       "      <td>0.008690</td>\n",
       "      <td>0.198788</td>\n",
       "      <td>0.003756</td>\n",
       "      <td>0.866046</td>\n",
       "      <td>0.005372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.479963</td>\n",
       "      <td>0.003980</td>\n",
       "      <td>1.158815</td>\n",
       "      <td>0.008671</td>\n",
       "      <td>0.195919</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.862390</td>\n",
       "      <td>0.005294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.480237</td>\n",
       "      <td>0.003945</td>\n",
       "      <td>1.158640</td>\n",
       "      <td>0.008677</td>\n",
       "      <td>0.193288</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.858664</td>\n",
       "      <td>0.005264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.479587</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>1.158505</td>\n",
       "      <td>0.008575</td>\n",
       "      <td>0.189944</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.854671</td>\n",
       "      <td>0.005516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.479512</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>1.158332</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>0.003705</td>\n",
       "      <td>0.851041</td>\n",
       "      <td>0.005562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.479963</td>\n",
       "      <td>0.003312</td>\n",
       "      <td>1.158111</td>\n",
       "      <td>0.008559</td>\n",
       "      <td>0.183778</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.847113</td>\n",
       "      <td>0.005116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.480162</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>1.157894</td>\n",
       "      <td>0.008644</td>\n",
       "      <td>0.180862</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.843361</td>\n",
       "      <td>0.005340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.479875</td>\n",
       "      <td>0.003179</td>\n",
       "      <td>1.157786</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.177837</td>\n",
       "      <td>0.003624</td>\n",
       "      <td>0.839551</td>\n",
       "      <td>0.005726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.479512</td>\n",
       "      <td>0.003825</td>\n",
       "      <td>1.157700</td>\n",
       "      <td>0.008649</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.835970</td>\n",
       "      <td>0.005418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.479563</td>\n",
       "      <td>0.003044</td>\n",
       "      <td>1.157556</td>\n",
       "      <td>0.008646</td>\n",
       "      <td>0.172306</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>0.832504</td>\n",
       "      <td>0.005302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.479362</td>\n",
       "      <td>0.003563</td>\n",
       "      <td>1.157438</td>\n",
       "      <td>0.008682</td>\n",
       "      <td>0.169775</td>\n",
       "      <td>0.002973</td>\n",
       "      <td>0.828989</td>\n",
       "      <td>0.004903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.479600</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>1.157414</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>0.167056</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.825256</td>\n",
       "      <td>0.004610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.479837</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>1.157445</td>\n",
       "      <td>0.008835</td>\n",
       "      <td>0.164337</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.821860</td>\n",
       "      <td>0.004439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.479788</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>1.157442</td>\n",
       "      <td>0.008899</td>\n",
       "      <td>0.161041</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>0.817826</td>\n",
       "      <td>0.004397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.479775</td>\n",
       "      <td>0.003513</td>\n",
       "      <td>1.157370</td>\n",
       "      <td>0.008878</td>\n",
       "      <td>0.158884</td>\n",
       "      <td>0.002929</td>\n",
       "      <td>0.814600</td>\n",
       "      <td>0.004417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.479400</td>\n",
       "      <td>0.004163</td>\n",
       "      <td>1.157335</td>\n",
       "      <td>0.008866</td>\n",
       "      <td>0.156272</td>\n",
       "      <td>0.003083</td>\n",
       "      <td>0.811143</td>\n",
       "      <td>0.004472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.479212</td>\n",
       "      <td>0.004260</td>\n",
       "      <td>1.157320</td>\n",
       "      <td>0.008864</td>\n",
       "      <td>0.153644</td>\n",
       "      <td>0.003197</td>\n",
       "      <td>0.807521</td>\n",
       "      <td>0.004290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.479125</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>1.157359</td>\n",
       "      <td>0.008901</td>\n",
       "      <td>0.150666</td>\n",
       "      <td>0.003265</td>\n",
       "      <td>0.803791</td>\n",
       "      <td>0.004189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.478962</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>1.157474</td>\n",
       "      <td>0.008946</td>\n",
       "      <td>0.148331</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>0.800152</td>\n",
       "      <td>0.004227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.479225</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>1.157454</td>\n",
       "      <td>0.009032</td>\n",
       "      <td>0.145787</td>\n",
       "      <td>0.003274</td>\n",
       "      <td>0.796882</td>\n",
       "      <td>0.004414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.479275</td>\n",
       "      <td>0.004204</td>\n",
       "      <td>1.157414</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>0.142934</td>\n",
       "      <td>0.003859</td>\n",
       "      <td>0.793092</td>\n",
       "      <td>0.004668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.479925</td>\n",
       "      <td>0.004154</td>\n",
       "      <td>1.157311</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>0.140872</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.789847</td>\n",
       "      <td>0.004779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.479512</td>\n",
       "      <td>0.003932</td>\n",
       "      <td>1.157206</td>\n",
       "      <td>0.009003</td>\n",
       "      <td>0.138290</td>\n",
       "      <td>0.003892</td>\n",
       "      <td>0.786256</td>\n",
       "      <td>0.005146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.479675</td>\n",
       "      <td>0.004153</td>\n",
       "      <td>1.157160</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.135597</td>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.782604</td>\n",
       "      <td>0.004924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    test-merror-mean  test-merror-std  test-mlogloss-mean  test-mlogloss-std  \\\n",
       "0           0.507662         0.002119            1.936791           0.001479   \n",
       "1           0.501650         0.004139            1.835599           0.003448   \n",
       "2           0.497200         0.003735            1.755325           0.004737   \n",
       "3           0.493800         0.003169            1.688766           0.005256   \n",
       "4           0.493750         0.004102            1.633064           0.005431   \n",
       "5           0.492362         0.004106            1.585748           0.005915   \n",
       "6           0.491350         0.003311            1.544547           0.006476   \n",
       "7           0.491250         0.002652            1.507688           0.006917   \n",
       "8           0.489800         0.002200            1.474918           0.006984   \n",
       "9           0.488650         0.003296            1.445872           0.007172   \n",
       "10          0.487988         0.003277            1.420126           0.007252   \n",
       "11          0.486950         0.003273            1.396999           0.007256   \n",
       "12          0.487225         0.003375            1.376303           0.007423   \n",
       "13          0.486838         0.002719            1.357715           0.007634   \n",
       "14          0.485750         0.003621            1.340694           0.007626   \n",
       "15          0.485687         0.003938            1.325375           0.007811   \n",
       "16          0.485000         0.003336            1.311447           0.007726   \n",
       "17          0.485000         0.003549            1.298823           0.007782   \n",
       "18          0.484400         0.003804            1.287546           0.007728   \n",
       "19          0.484650         0.003131            1.277022           0.007887   \n",
       "20          0.484438         0.002687            1.267365           0.007932   \n",
       "21          0.483700         0.002961            1.258529           0.007686   \n",
       "22          0.483362         0.003120            1.250384           0.007704   \n",
       "23          0.482750         0.002748            1.242965           0.007854   \n",
       "24          0.482437         0.002902            1.236390           0.007895   \n",
       "25          0.482213         0.002646            1.230182           0.007910   \n",
       "26          0.482337         0.003118            1.224528           0.008014   \n",
       "27          0.482150         0.003216            1.219456           0.007941   \n",
       "28          0.481738         0.003410            1.214718           0.007986   \n",
       "29          0.481900         0.003143            1.210479           0.007927   \n",
       "..               ...              ...                 ...                ...   \n",
       "61          0.480600         0.004320            1.160830           0.008743   \n",
       "62          0.480637         0.004829            1.160431           0.008816   \n",
       "63          0.480525         0.004243            1.160080           0.008751   \n",
       "64          0.480375         0.003934            1.159823           0.008830   \n",
       "65          0.480300         0.003812            1.159527           0.008730   \n",
       "66          0.480088         0.004168            1.159245           0.008724   \n",
       "67          0.480075         0.004421            1.158970           0.008690   \n",
       "68          0.479963         0.003980            1.158815           0.008671   \n",
       "69          0.480237         0.003945            1.158640           0.008677   \n",
       "70          0.479587         0.003485            1.158505           0.008575   \n",
       "71          0.479512         0.003379            1.158332           0.008573   \n",
       "72          0.479963         0.003312            1.158111           0.008559   \n",
       "73          0.480162         0.003263            1.157894           0.008644   \n",
       "74          0.479875         0.003179            1.157786           0.008645   \n",
       "75          0.479512         0.003825            1.157700           0.008649   \n",
       "76          0.479563         0.003044            1.157556           0.008646   \n",
       "77          0.479362         0.003563            1.157438           0.008682   \n",
       "78          0.479600         0.003687            1.157414           0.008726   \n",
       "79          0.479837         0.003870            1.157445           0.008835   \n",
       "80          0.479788         0.003370            1.157442           0.008899   \n",
       "81          0.479775         0.003513            1.157370           0.008878   \n",
       "82          0.479400         0.004163            1.157335           0.008866   \n",
       "83          0.479212         0.004260            1.157320           0.008864   \n",
       "84          0.479125         0.004199            1.157359           0.008901   \n",
       "85          0.478962         0.004626            1.157474           0.008946   \n",
       "86          0.479225         0.004604            1.157454           0.009032   \n",
       "87          0.479275         0.004204            1.157414           0.009033   \n",
       "88          0.479925         0.004154            1.157311           0.009099   \n",
       "89          0.479512         0.003932            1.157206           0.009003   \n",
       "90          0.479675         0.004153            1.157160           0.008960   \n",
       "\n",
       "    train-merror-mean  train-merror-std  train-mlogloss-mean  \\\n",
       "0            0.458653          0.002688             1.928032   \n",
       "1            0.435650          0.002800             1.818443   \n",
       "2            0.420594          0.001374             1.730046   \n",
       "3            0.410869          0.001569             1.655890   \n",
       "4            0.401909          0.000908             1.593027   \n",
       "5            0.395597          0.001165             1.538604   \n",
       "6            0.389137          0.001178             1.490676   \n",
       "7            0.382791          0.000915             1.447380   \n",
       "8            0.377150          0.001569             1.408386   \n",
       "9            0.372106          0.001042             1.373230   \n",
       "10           0.367519          0.000729             1.341698   \n",
       "11           0.362631          0.000688             1.312758   \n",
       "12           0.358731          0.000957             1.286339   \n",
       "13           0.354753          0.000512             1.262517   \n",
       "14           0.351091          0.000554             1.240523   \n",
       "15           0.347228          0.000764             1.220359   \n",
       "16           0.343566          0.001003             1.201379   \n",
       "17           0.340191          0.000664             1.184032   \n",
       "18           0.336728          0.000588             1.168169   \n",
       "19           0.333366          0.000524             1.152812   \n",
       "20           0.329828          0.000495             1.138851   \n",
       "21           0.326684          0.000512             1.125635   \n",
       "22           0.323884          0.000616             1.113482   \n",
       "23           0.321428          0.000578             1.102013   \n",
       "24           0.319184          0.000718             1.091539   \n",
       "25           0.316041          0.001161             1.081177   \n",
       "26           0.313003          0.001315             1.071584   \n",
       "27           0.310219          0.001526             1.062853   \n",
       "28           0.307853          0.001707             1.054758   \n",
       "29           0.304878          0.002123             1.046835   \n",
       "..                ...               ...                  ...   \n",
       "61           0.216541          0.003736             0.889058   \n",
       "62           0.213253          0.003580             0.884978   \n",
       "63           0.210416          0.003728             0.881156   \n",
       "64           0.207244          0.003640             0.877089   \n",
       "65           0.203916          0.003293             0.873222   \n",
       "66           0.200772          0.003594             0.869109   \n",
       "67           0.198788          0.003756             0.866046   \n",
       "68           0.195919          0.003667             0.862390   \n",
       "69           0.193288          0.003595             0.858664   \n",
       "70           0.189944          0.003496             0.854671   \n",
       "71           0.186900          0.003705             0.851041   \n",
       "72           0.183778          0.002982             0.847113   \n",
       "73           0.180862          0.003058             0.843361   \n",
       "74           0.177837          0.003624             0.839551   \n",
       "75           0.175009          0.003459             0.835970   \n",
       "76           0.172306          0.003664             0.832504   \n",
       "77           0.169775          0.002973             0.828989   \n",
       "78           0.167056          0.002761             0.825256   \n",
       "79           0.164337          0.002710             0.821860   \n",
       "80           0.161041          0.002777             0.817826   \n",
       "81           0.158884          0.002929             0.814600   \n",
       "82           0.156272          0.003083             0.811143   \n",
       "83           0.153644          0.003197             0.807521   \n",
       "84           0.150666          0.003265             0.803791   \n",
       "85           0.148331          0.003621             0.800152   \n",
       "86           0.145787          0.003274             0.796882   \n",
       "87           0.142934          0.003859             0.793092   \n",
       "88           0.140872          0.003650             0.789847   \n",
       "89           0.138290          0.003892             0.786256   \n",
       "90           0.135597          0.003678             0.782604   \n",
       "\n",
       "    train-mlogloss-std  \n",
       "0             0.000488  \n",
       "1             0.002313  \n",
       "2             0.001781  \n",
       "3             0.001782  \n",
       "4             0.001410  \n",
       "5             0.001142  \n",
       "6             0.001523  \n",
       "7             0.001344  \n",
       "8             0.001138  \n",
       "9             0.000783  \n",
       "10            0.001280  \n",
       "11            0.001003  \n",
       "12            0.001164  \n",
       "13            0.001068  \n",
       "14            0.001233  \n",
       "15            0.001286  \n",
       "16            0.001539  \n",
       "17            0.001527  \n",
       "18            0.001562  \n",
       "19            0.001366  \n",
       "20            0.001500  \n",
       "21            0.001811  \n",
       "22            0.002104  \n",
       "23            0.002112  \n",
       "24            0.002274  \n",
       "25            0.002454  \n",
       "26            0.002251  \n",
       "27            0.002127  \n",
       "28            0.002265  \n",
       "29            0.002398  \n",
       "..                 ...  \n",
       "61            0.004937  \n",
       "62            0.004968  \n",
       "63            0.005173  \n",
       "64            0.005135  \n",
       "65            0.004902  \n",
       "66            0.005158  \n",
       "67            0.005372  \n",
       "68            0.005294  \n",
       "69            0.005264  \n",
       "70            0.005516  \n",
       "71            0.005562  \n",
       "72            0.005116  \n",
       "73            0.005340  \n",
       "74            0.005726  \n",
       "75            0.005418  \n",
       "76            0.005302  \n",
       "77            0.004903  \n",
       "78            0.004610  \n",
       "79            0.004439  \n",
       "80            0.004397  \n",
       "81            0.004417  \n",
       "82            0.004472  \n",
       "83            0.004290  \n",
       "84            0.004189  \n",
       "85            0.004227  \n",
       "86            0.004414  \n",
       "87            0.004668  \n",
       "88            0.004779  \n",
       "89            0.005146  \n",
       "90            0.004924  \n",
       "\n",
       "[91 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KCgpq7HiV6QI7292PqWjdXj7fEZjo7j3K2XYmoV1jGNAXuN/dT0hynLToAlsV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\n1h8Y6+5Dovc/A3D3O8rsdxr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33ea2e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W+R2VMcZ0nJbqJPqKyBggQ0QOFpFDvKEA6Bbh9gcAoSX367x5Tff1IxFZCdwB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3401f4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results_xgbclassifier2 = xgbclassifier_model2.evals_result()\n",
    "epochs = len(results_xgbclassifier2['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.12575\n",
      "Test accuracy at  -18 dB = 0.1275\n",
      "Test accuracy at  -16 dB = 0.11875\n",
      "Test accuracy at  -14 dB = 0.1265\n",
      "Test accuracy at  -12 dB = 0.145\n",
      "Test accuracy at  -10 dB = 0.173\n",
      "Test accuracy at  -8 dB = 0.256\n",
      "Test accuracy at  -6 dB = 0.34775\n",
      "Test accuracy at  -4 dB = 0.3995\n",
      "Test accuracy at  -2 dB = 0.4805\n",
      "Test accuracy at  0 dB = 0.598\n",
      "Test accuracy at  2 dB = 0.70475\n",
      "Test accuracy at  4 dB = 0.8245\n",
      "Test accuracy at  6 dB = 0.856\n",
      "Test accuracy at  8 dB = 0.8555\n",
      "Test accuracy at  10 dB = 0.86\n",
      "Test accuracy at  12 dB = 0.86025\n",
      "Test accuracy at  14 dB = 0.8615\n",
      "Test accuracy at  16 dB = 0.85975\n",
      "Test accuracy at  18 dB = 0.86575\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = xgbclassifier_model2.predict(X_32test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_32_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TQQGVxaI+N3xVNxNCbTMkyGQyXL9+HadOncLPP/+sVuBaW1vXuCqZqkev4gYGBlZ7Q1PH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c341955f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.88  0.00   0.00  0.00  0.01   0.05   0.05  0.01\n",
      "BPSK   0.00  0.99   0.00  0.00  0.00   0.00   0.00  0.01\n",
      "CPFSK  0.00  0.00   0.99  0.00  0.00   0.01   0.01  0.00\n",
      "GFSK   0.01  0.00   0.00  0.99  0.00   0.00   0.00  0.00\n",
      "PAM4   0.00  0.01   0.00  0.00  0.97   0.01   0.00  0.00\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.56   0.43  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.42   0.56  0.01\n",
      "QPSK   0.02  0.00   0.00  0.00  0.00   0.04   0.01  0.93\n"
     ]
    },
    {
     "data": {
      "image/png": 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dd9SqVYtZs2Zp5J86dSqffPIJ7777LitXrixz9qgQQlR1pjxUV6VSaQxnlihJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33e9fbeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_32_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune trade-off between learning_rate and num_boost_round\n",
    "Low learning_rate implies more trees must be added to the ensemble; learning_rate is commonly set between 0.1 and 0.3\n",
    "and also less than 0.1. Our learning_rate of 0.1 found via grid search is high and suggests that the default no. of trees 100 (during grid search) is low and more trees should be added. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 287.1152663588524 minutes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: 0.50915, std: 0.00124, params: {'n_estimators': 100, 'learning_rate': 0.0001},\n",
       "  mean: 0.51045, std: 0.00119, params: {'n_estimators': 200, 'learning_rate': 0.0001},\n",
       "  mean: 0.51039, std: 0.00150, params: {'n_estimators': 300, 'learning_rate': 0.0001},\n",
       "  mean: 0.51005, std: 0.00125, params: {'n_estimators': 400, 'learning_rate': 0.0001},\n",
       "  mean: 0.51025, std: 0.00098, params: {'n_estimators': 500, 'learning_rate': 0.0001},\n",
       "  mean: 0.51025, std: 0.00181, params: {'n_estimators': 100, 'learning_rate': 0.001},\n",
       "  mean: 0.51144, std: 0.00158, params: {'n_estimators': 200, 'learning_rate': 0.001},\n",
       "  mean: 0.51181, std: 0.00173, params: {'n_estimators': 300, 'learning_rate': 0.001},\n",
       "  mean: 0.51179, std: 0.00153, params: {'n_estimators': 400, 'learning_rate': 0.001},\n",
       "  mean: 0.51256, std: 0.00069, params: {'n_estimators': 500, 'learning_rate': 0.001},\n",
       "  mean: 0.51371, std: 0.00065, params: {'n_estimators': 100, 'learning_rate': 0.01},\n",
       "  mean: 0.51716, std: 0.00049, params: {'n_estimators': 200, 'learning_rate': 0.01},\n",
       "  mean: 0.51950, std: 0.00023, params: {'n_estimators': 300, 'learning_rate': 0.01},\n",
       "  mean: 0.52058, std: 0.00024, params: {'n_estimators': 400, 'learning_rate': 0.01},\n",
       "  mean: 0.52085, std: 0.00083, params: {'n_estimators': 500, 'learning_rate': 0.01},\n",
       "  mean: 0.52065, std: 0.00231, params: {'n_estimators': 100, 'learning_rate': 0.1},\n",
       "  mean: 0.52035, std: 0.00304, params: {'n_estimators': 200, 'learning_rate': 0.1},\n",
       "  mean: 0.51720, std: 0.00256, params: {'n_estimators': 300, 'learning_rate': 0.1},\n",
       "  mean: 0.51677, std: 0.00245, params: {'n_estimators': 400, 'learning_rate': 0.1},\n",
       "  mean: 0.51726, std: 0.00252, params: {'n_estimators': 500, 'learning_rate': 0.1}],\n",
       " {'learning_rate': 0.01, 'n_estimators': 500},\n",
       " 0.52085)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid10 = {'learning_rate': [0.0001, 0.001, 0.01, 0.1], 'n_estimators': [100, 200, 300, 400, 500]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "\n",
    "grid_search10 = GridSearchCV(estimator = XGBClassifier(max_depth= 11, min_child_weight= 6, gamma = 0.0, subsample = 0.75, \n",
    "                              colsample_bytree = 0.85, reg_alpha= 1e-05, reg_lambda= 1, \n",
    "                              objective='multi:softprob', num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid10, scoring= 'accuracy', n_jobs=4)\n",
    "\n",
    "grid_search10.fit(X_32train_new, y_32_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))\n",
    "\n",
    "grid_search10.grid_scores_, grid_search10.best_params_, grid_search10.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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 },
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